Remote regulation and control system of digital power supply

Abstract

The invention discloses a remote regulation and control system for a digital power supply, and relates to the field of digital power supplies, and the system comprises a power storage module which is used for continuously supplying power and storing energy, executing remote communication, and receiving a control instruction; the communication module is used for providing a wireless communication medium connected with the Internet and opening remote communication control authority with specified equipment; through automatic data acquisition measures, voltage, current, power, temperature and load data are acquired according to a preset period, fusion and preprocessing are carried out, comprehensiveness and accuracy of the data are ensured, accurate identification and processing of abnormal data are ensured through multi-level analysis, verification, modeling and evaluation, and the automatic identification and regulation mechanism has the advantages of high accuracy and high reliability. According to the method, a response can be quickly made in an abnormal environment, historical data training and simulation are performed through machine learning and artificial intelligence technologies, various processing strategies are provided, evaluation and sorting are performed in a virtual environment, and effectiveness and optimality of the strategies are ensured.

Description

Technical Field

[0001] This invention relates to the field of digital power technology, specifically to a remote control system for digital power supplies. Background Technology

[0002] A digital power supply is a digitally controlled power supply device that can be remotely monitored and precisely controlled and regulated in terms of output voltage, current, power and other parameters through the Internet and smart technology. Digital power supplies are characterized by high precision, high stability, high reliability, high efficiency, remote control and automatic regulation. With the continuous advancement of digital technology, digital power supplies have become a development trend in the power supply field and are widely used in the communications industry, industrial automation, medical industry, aerospace and research and education.

[0003] However, during the use of digital power systems, network or equipment failures often lead to delays or reliability issues. Data processing and analysis efficiency is limited under these conditions, making it prone to errors and lacking comprehensive analysis of collected data, especially effective identification and processing of abnormal data.

[0004] The handling of abnormal situations is mostly a passive response, the data anomaly handling methods are simplistic, it is easy to overlook potential complex problems, and there is a lack of intelligent means to effectively predict and prevent problems. When abnormal situations occur, there is a lack of trial implementation measures for remedial strategies, which makes the control process for anomalies complex and time-consuming. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a remote control system for digital power supply, which can effectively solve the problems of the existing technology.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] This invention discloses a remote control system for a digital power supply, comprising:

[0010] Energy storage modules are used for continuous power supply and energy storage, as well as for receiving remote communication and control commands.

[0011] The communication module provides a wireless communication medium for connecting to the Internet and grants remote communication control permissions to designated devices.

[0012] The instruction control module is a microprocessor or single-chip microcomputer responsible for processing data and executing control commands. It edits and submits instructions for switching, adjusting parameters, and changing modes of the energy storage module.

[0013] The data acquisition module is used to collect voltage, current, power, temperature and load data of the energy storage module and communication module at preset cycles, and then convert them into machine-readable language after fusion and preprocessing to form the data acquisition dataset.

[0014] A control unit is used to correct abnormal data. The control unit includes an anomaly detection module, a configuration verification module, a simulation processing module, and an evaluation module. The anomaly detection module and the configuration verification module are interconnected via a wireless network. The configuration verification module and the simulation processing module are interconnected via a wireless network. The simulation processing module and the evaluation module are interconnected via a wireless network.

[0015] The anomaly detection module is used to analyze the collected dataset, detect the presence of abnormal data based on a preset standard data template, classify and categorize the abnormal data, trace the related data of the abnormal data, and record the time, environment and equipment status information of the anomaly.

[0016] The configuration verification module is used to verify the configuration of devices based on the associated data of the abnormal data traced by the abnormal identification module, and to record the time, environment and equipment status information of the abnormality. The module checks the operating parameters of the associated devices, performs equipment configuration verification, and constructs a verification dataset from the verification results.

[0017] The simulation processing module is used to build simulation models for neural network algorithms. It trains the models with historical data, analyzes the collected datasets and validation datasets input into the simulation models, and outputs several simulation processing strategies.

[0018] The evaluation module is used to evaluate the performance of several processing strategies proposed by the simulation processing module in a preset virtual environment, sort them according to their performance, and push them to the instruction control module. The instruction control module receives and applies the processing strategy with the best performance evaluation submitted by the evaluation module, and uses the processing strategy with the second best performance evaluation as a backup strategy.

[0019] The data storage module is used to store all operational data, abnormal data, processing strategies, and evaluation results using a cloud server.

[0020] Furthermore, during the process of detecting the existence of abnormal data based on the preset standard data template, the anomaly identification module defines the data that cannot be matched in the collected dataset as unclassified abnormal data, extracts features from the unclassified abnormal data, compares the features of the unclassified abnormal data with the features of historical data, matches historical data samples that hit the similarity threshold, and if a match still cannot be found, submits the unclassified abnormal data to the configuration verification module and receives several processing strategies pushed by the evaluation module. The unclassified abnormal data judged to be successfully regulated and its corresponding processing strategy are integrated and saved, and dynamically updated to the preset reference of the standard data template.

[0021] Furthermore, the verification content of the device configuration verification module includes: checking the device settings, calibration data, operating mode, and output power.

[0022] Furthermore, during model training, the simulation processing module preprocesses and normalizes historical data, then divides the normalized historical data into an 80% training set, a 10% validation set, and a 10% test set. The training set is used for training, network weights are adjusted, predicted values ​​are calculated through forward propagation, weights are updated through backpropagation, and model performance is monitored through the validation set. The number of training cycles and batch size are preset. After each training cycle, the model's performance on the validation set is evaluated, and the model's generalization performance is evaluated using the test set. The training parameters or model structure are adjusted based on the feedback results from the validation and test sets.

[0023] Furthermore, during the virtual environment preset by the evaluation module, power devices, load models and power network topology are created using modeling tools. Policy activation conditions, execution cycles and thresholds are set for the simulation processing strategy. In the current virtual environment, the simulation processing strategy is executed, and response time, power consumption and efficiency are evaluated.

[0024] Furthermore, the evaluation module assesses the performance of the current virtual environment by calculating the degree of simulation between the output of the preset virtual environment and the output of the final environment. The formula for calculating the degree of simulation is as follows:

[0025]

[0026] MSE stands for Mean Squared Error, and its calculation formula is as follows:

[0027] In the formula, S represents the simulation index, n represents the total number of result values, and V i A represents the i-th output value in the virtual environment. i This represents the i-th output value in the actual environment. This represents the average value of the output results in the actual environment.

[0028] Furthermore, the control unit is interactively connected to a tagging module via a wireless network. The tagging module is used to receive the verification dataset obtained by the configuration verification module, obtain device information containing abnormal data, and perform tagging processing.

[0029] Furthermore, the operation of the marking module includes:

[0030] a. Receive a verification dataset containing abnormal data, parse the verification dataset, and extract device information related to the abnormality;

[0031] b. Based on the extracted information, mark the devices with abnormal data according to the type of abnormality, the severity of the abnormality, the priority, and the current status of the device, and store the marked device information in the data storage module;

[0032] c. Monitor the operating status of the marking device in real time, and update the marking information if an anomaly is detected again.

[0033] Furthermore, the marking module is interconnected with a threshold setting module via a wireless network. The threshold setting module is interconnected with the acquisition module via a wireless network. The threshold setting module is used to receive marking processing information from the marking module, obtain control permissions for the marked object, and adjust the acquisition frequency of the marked object in the next cycle according to a preset adjustment standard.

[0034] Furthermore, the energy storage module, communication module, and data acquisition module are interconnected via a wireless network; the communication module is interconnected with the command control module via a wireless network; the data acquisition module is interconnected with the regulation unit via a wireless network; and the command control module, regulation unit, and data storage module are interconnected via a wireless network.

[0035] (III) Beneficial Effects

[0036] Compared with known prior art, the technical solution provided by this invention has the following beneficial effects:

[0037] 1. Through automated data acquisition measures, voltage, current, power, temperature and load data are collected according to a preset cycle, and then fused and preprocessed to ensure the comprehensiveness and accuracy of the data. Through multi-level analysis, verification, modeling and evaluation, the system ensures the accurate identification and processing of abnormal data. This autonomous identification and control mechanism can react quickly in abnormal environments, reduce human intervention and improve the reliability and intelligence level of the digital power system.

[0038] 2. The system employs a neural network simulation model module, which uses machine learning and artificial intelligence technologies to train and simulate historical data, providing multiple processing strategies. These strategies are then evaluated and ranked in a virtual environment to ensure their effectiveness and optimality. The modular anomaly handling method enhances the intelligence and responsiveness of anomaly handling. The continuous learning and model update mechanism enables the system to self-optimize and evolve, improving its long-term stability and efficiency.

[0039] 3. By verifying device settings, calibration data, operating modes, and output power, the system can mark and monitor devices with abnormal data in detail, ensuring timely response and handling of devices after anomalies occur. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0041] Figure 1 This is a schematic diagram of the framework of the present invention.

[0042] The labels in the diagram represent: 1. Energy storage module; 2. Communication module; 3. Command control module; 4. Data acquisition module; 5. Regulation unit; 51. Anomaly detection module; 52. Configuration verification module; 53. Simulation processing module; 54. Evaluation module; 6. Data storage module; 7. Tagging module; 8. Threshold setting module. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0044] The present invention will be further described below with reference to embodiments.

[0045] Example 1

[0046] This embodiment provides a remote control system for a digital power supply, such as... Figure 1 As shown, it includes:

[0047] Energy storage module 1 is used for continuous power supply and energy storage, receiving remote communication and control commands, storing and supplying power, and also monitoring power, temperature and operating status.

[0048] Communication module 2 is used to provide a wireless communication medium for connecting to the Internet and to grant remote communication control permissions to designated devices;

[0049] The instruction control module 3 is a microprocessor or single-chip microcomputer responsible for processing data and executing control commands. It edits and submits instructions for switching, adjusting parameters, and switching modes of the energy storage module 1, and receives feedback on the device's operating status and alarm information.

[0050] The acquisition module 4 is used to collect voltage, current, power, temperature and load data from the energy storage module 1 and the communication module 2 at a preset period. After fusion and preprocessing, the data is converted into machine-readable language, including data denoising and filtering, to ensure the accuracy and reliability of the data and form an acquisition dataset. After the data is collected by the sensors and data acquisition device, it is sent to the command control module 3 through the communication module 2 for monitoring and analysis.

[0051] Control unit 5 is used to correct abnormal data, analyze the collected data, and detect whether there are any abnormalities.

[0052] Data storage module 6 is used to store all operational data, abnormal data, processing strategies and evaluation results using a cloud server. The data can be stored for a long time, facilitating historical data analysis, trend prediction and report generation.

[0053] The energy storage module 1, communication module 2, and data acquisition module 4 are interconnected via a wireless network. The communication module 2 is interconnected with the command control module 3 via a wireless network. The data acquisition module 4 is interconnected with the control unit 5 via a wireless network. The command control module 3, control unit 5, and data storage module 6 are interconnected via a wireless network.

[0054] Compared with existing technologies, it can adjust and optimize power management in real time, improve the system's response speed and accuracy, not only improve the intelligence level and remote control capability of power management, but also enhance the system's reliability, data accuracy and self-diagnostic capability, prevent false alarms and misoperations, and has high application value.

[0055] Example 2

[0056] At other levels, this embodiment also provides a measure to correct abnormal data, such as... Figure 1As shown, the control unit 5 includes an anomaly detection module 51, a configuration verification module 52, a simulation processing module 53, and an evaluation module 54. The anomaly detection module 51 and the configuration verification module 52 are interconnected via a wireless network. The configuration verification module 52 and the simulation processing module 53 are interconnected via a wireless network. The simulation processing module 53 and the evaluation module 54 are interconnected via a wireless network.

[0057] The anomaly identification module 51 is used to analyze the collected dataset, detect the existence of abnormal data based on the preset standard data template, classify and categorize the abnormal data, trace the related data of the abnormal data, and record the time, environment and equipment status information of the anomaly. It systematically records and forms a detailed anomaly data archive to facilitate tracing and subsequent processing.

[0058] In the process of detecting the existence of abnormal data based on the preset standard data template, the anomaly identification module 51 defines the data that cannot be matched in the collected dataset as unclassified abnormal data. It extracts features from the unclassified abnormal data, compares the features of the unclassified abnormal data with the features of historical data, and matches historical data samples that hit the similarity threshold. If no match is found, the unclassified abnormal data is submitted to the configuration verification module 52 and receives several processing strategies pushed by the evaluation module 54. The unclassified abnormal data that is judged to be successfully regulated and its corresponding processing strategy are integrated and saved, and dynamically updated to the preset reference of the standard data template. This improves the intelligence and automation level of anomaly detection and ensures the comprehensiveness and accuracy of anomaly data processing.

[0059] The configuration verification module 52 is used to verify the equipment configuration based on the associated data of the abnormal data traced by the abnormal identification module 51, and to record the time, environment and equipment status information of the abnormality. The verification results constitute a verification dataset. The verification content includes checking the equipment setpoints, calibration data, working mode and output power to ensure the accuracy and stability of the equipment configuration, thereby reducing the risk of equipment failure and abnormal operation.

[0060] The simulation processing module 53 is used to build a simulation model for neural network algorithms, train the model with historical data, analyze the collected dataset and the validation dataset by inputting them into the simulation model, and output several simulation processing strategies.

[0061] During model training, the simulation processing module 53 preprocesses and normalizes historical data, then divides the normalized historical data into an 80% training set, a 10% validation set, and a 10% test set. The training set is used for training, network weights are adjusted, predicted values ​​are calculated through forward propagation, weights are updated through backpropagation, and model performance is monitored through the validation set. The number of training cycles and batch size are preset. After each training cycle, the model's performance on the validation set is evaluated, and the generalization performance of the model is evaluated using the test set. Based on the feedback results from the validation and test sets, the training parameters or model structure are adjusted. This effectively utilizes historical data for training and optimization, providing accurate analysis and feasible processing strategies for remote control of digital power supplies.

[0062] Evaluation module 54 is used to evaluate the performance of several processing strategies proposed by simulation processing module 53 in a preset virtual environment, sort them according to their performance and push them to instruction control module 3. Instruction control module 3 receives and applies the processing strategy with the best performance evaluation submitted by evaluation module 54, and uses the processing strategy with the second best performance evaluation as a backup strategy.

[0063] During the evaluation of the preset virtual environment in module 54, power equipment, load models, and power network topology are created using modeling tools. The activation conditions, execution cycle, and thresholds of the simulation processing strategy are set. The simulation processing strategy is executed in the current virtual environment, and the response time, power consumption, and efficiency are evaluated. By calculating the degree of simulation between the output results of the virtual environment and the output results of the actual environment, the effectiveness and reliability of the processing strategy are systematically evaluated to ensure that the final applied strategy has high performance.

[0064] Evaluation module 54 evaluates the performance of the virtual environment in the current cycle by calculating the degree of simulation between the output of the preset virtual environment and the output of the final environment. The formula for calculating the degree of simulation is as follows:

[0065]

[0066] MSE stands for Mean Squared Error, and its calculation formula is as follows:

[0067] In the formula, S represents the simulation index, n represents the total number of result values, and V i A represents the i-th output value in the virtual environment. i This represents the i-th output value in the actual environment. It represents the average of the output results in the actual environment, thus enabling a systematic evaluation of the similarity between the output results of the virtual environment and the actual environment, thereby determining the effectiveness and reliability of the simulation processing strategy in the virtual environment.

[0068] Compared with existing technologies, this method significantly improves the accuracy of data anomaly detection, the comprehensiveness of equipment configuration verification, the intelligence of processing strategies, and the efficiency of application effects through multi-level data analysis and processing, intelligent model training and optimization, and rigorous virtual environment evaluation, thus outperforming existing technologies.

[0069] Example 3

[0070] In this embodiment, as Figure 1 As shown, the control unit 5 is interconnected with the marking module 7 via a wireless network. The marking module 7 receives the verification dataset obtained by the configuration verification module 52, acquires device information containing abnormal data, and performs marking processing to improve the timeliness of device anomaly detection and processing. The working process of the marking module 7 includes:

[0071] a. Receive a verification dataset containing abnormal data, parse the verification dataset, and extract device information related to the abnormality;

[0072] b. Based on the extracted information, mark the devices with abnormal data according to the abnormality type, severity priority, and current status, and store the marked device information in the data storage module 6;

[0073] c. Monitor the operating status of the marking device in real time, and update the marking information if an anomaly is detected again.

[0074] The marking module 7 is interconnected with the threshold setting module 8 via a wireless network. The threshold setting module 8 is interconnected with the acquisition module 4 via a wireless network. The threshold setting module 8 is used to receive the marking processing information from the marking module 7, obtain the control permissions of the marked object, and adjust the acquisition frequency of the marked object in the next cycle according to the preset adjustment standard, so as to ensure the relevance and effectiveness of data acquisition and improve the monitoring accuracy of the overall system.

[0075] Compared with existing technologies, the real-time processing and tagging mechanism can quickly respond to and identify abnormal conditions of the equipment, avoiding the risks that may be caused by delayed processing in traditional methods. The multi-dimensional tagging system can more accurately describe the abnormal conditions of the equipment, which helps subsequent analysis and processing, and improves the accuracy and efficiency of abnormal handling. The continuous monitoring and dynamic updating mechanism can ensure the real-time nature of equipment status information. By dynamically adjusting the acquisition frequency, the system can better balance resource utilization and data acquisition needs, and improve the system's flexibility and adaptability.

[0076] In summary, this invention collects voltage, current, power, temperature, and load data according to a preset cycle through automated data acquisition, and performs data fusion and preprocessing to ensure the comprehensiveness and accuracy of the data. Through multi-level analysis, verification, modeling, and evaluation, the system can accurately identify and process abnormal data. Through this autonomous identification and control mechanism, it can react quickly in abnormal environments, reduce human intervention, and improve the reliability and intelligence level of the digital power supply system.

[0077] This invention employs a neural network simulation model, utilizing machine learning and artificial intelligence technologies for training and simulation with historical data. It provides multiple processing strategies, which are evaluated and ranked in a virtual environment to ensure their effectiveness and optimality. The modular anomaly handling approach enhances the intelligence and responsiveness of anomaly handling. Continuous learning and model update mechanisms enable the system to self-optimize and evolve, thereby improving long-term operational stability and efficiency. By verifying device setpoints, calibration data, operating modes, and output power, the system can meticulously flag and monitor devices exhibiting abnormal data, ensuring timely response and handling after anomalies occur.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote control system for a digital power supply, characterized in that, include: Energy storage module (1) is used for continuous power supply and energy storage, and for receiving remote communication and control commands; Communication module (2) is used to provide a wireless communication medium for connecting to the Internet and to grant remote communication control permissions to designated devices; The instruction control module (3) is a microprocessor or single-chip microcomputer responsible for processing data and executing control commands. It performs on / off control, parameter adjustment and mode switching instructions for the energy storage module (1). The acquisition module (4) is used to acquire voltage, current, power, temperature and load data of the energy storage module (1) and the communication module (2) according to a preset cycle, and after fusion and preprocessing, convert them into machine-readable language to form the acquisition dataset. A control unit (5) is used to correct abnormal data. The control unit (5) includes an anomaly identification module (51), a configuration verification module (52), a simulation processing module (53), and an evaluation module (54). The anomaly identification module (51) and the configuration verification module (52) are interconnected via a wireless network. The configuration verification module (52) and the simulation processing module (53) are interconnected via a wireless network. The simulation processing module (53) and the evaluation module (54) are interconnected via a wireless network. The anomaly identification module (51) is used to analyze the collected dataset, detect whether there is abnormal data based on the preset standard data template, classify and categorize the abnormal data, trace the related data of the abnormal data, and record the time, environment and equipment status information of the anomaly. The configuration verification module (52) is used to verify the operating parameters of the associated equipment based on the associated data of the abnormal data traced by the abnormal identification module (51), and to record the time, environment and equipment status information of the abnormality, and to perform equipment configuration verification, and to form a verification dataset by the verification results. The simulation processing module (53) is used to build a simulation model using neural network algorithms, train the model with historical data, analyze the collected dataset and the verification dataset by inputting them into the simulation model, and output several simulation processing strategies. The evaluation module (54) is used to evaluate the performance of several processing strategies proposed by the simulation processing module (53) through a preset virtual environment, sort them according to their performance and push them to the instruction control module (3). The instruction control module (3) receives and applies the processing strategy with the best performance evaluation submitted by the evaluation module (54) and uses the processing strategy with the second-best performance evaluation as a backup strategy. The data storage module (6) is used to store all running data, abnormal data, processing strategies and evaluation results using a cloud server.

2. The remote control system for a digital power supply according to claim 1, characterized in that, In the process of detecting whether there is abnormal data based on the preset standard data template, the anomaly identification module (51) defines the data that cannot be matched in the collected dataset as unclassified abnormal data, extracts features from the unclassified abnormal data, compares the features of the unclassified abnormal data with the features of historical data, matches historical data samples that hit the similarity threshold, and if it still cannot be matched, submits the unclassified abnormal data to the configuration verification module (52) and receives several processing strategies pushed by the evaluation module (54), integrates and saves the unclassified abnormal data that is judged to be successfully regulated and its corresponding processing strategy, and dynamically updates it to the preset reference of the standard data template.

3. The remote control system for a digital power supply according to claim 1, characterized in that, The verification content of the configuration verification module (52) includes: checking the device settings, calibration data, working mode and output power.

4. The remote control system for a digital power supply according to claim 1, characterized in that, During the model training process, the simulation processing module (53) preprocesses and normalizes the historical data, divides the normalized historical data into an 80% training set, a 10% validation set, and a 10% test set, uses the training set for training, adjusts the network weights, calculates the predicted value through forward propagation, updates the weights through backpropagation, monitors the model performance through the validation set, presets the number of training cycles and batch size, evaluates the model's performance on the validation set after each training cycle, evaluates the model's generalization performance using the test set, and adjusts the training parameters or model structure based on the feedback results from the validation set and test set.

5. The remote control system for a digital power supply according to claim 1, characterized in that, In the virtual environment preset by the evaluation module (54), power devices, load models and power network topology are created by modeling tools. The activation conditions, execution cycle and threshold of the simulation processing strategy are set. The simulation processing strategy is executed in the current virtual environment, and the response time, power consumption and efficiency are evaluated.

6. The remote control system for a digital power supply according to claim 1, characterized in that, The evaluation module (54) evaluates the performance of the current cycle virtual environment by calculating the simulation degree between the output result of the preset virtual environment and the output result represented by the final environment. The simulation degree calculation formula is as follows: MSE stands for Mean Squared Error, and its calculation formula is as follows: In the formula, S represents the simulation index, n represents the total number of result values, and V i A represents the i-th output value in the virtual environment. i This represents the i-th output value in the actual environment. This represents the average value of the output results in the actual environment.

7. The remote control system for a digital power supply according to claim 6, characterized in that, The control unit (5) is connected to the tagging module (7) via a wireless network. The tagging module (7) is used to receive the verification dataset obtained by the configuration verification module (52), obtain the device information with abnormal data, and perform tagging processing.

8. The remote control system for a digital power supply according to claim 7, characterized in that, The operation of the marking module (7) includes: a. Receive a verification dataset containing abnormal data, parse the verification dataset, and extract device information related to the abnormality; b. Based on the extracted information, mark the devices with abnormal data according to the abnormal type, severity priority and current status, and store the marked device information in the data storage module (6); c. Monitor the operating status of the marking device in real time, and update the marking information if an anomaly is detected again.

9. A remote control system for a digital power supply according to claim 7, characterized in that, The marking module (7) is interactively connected to the threshold setting module (8) via a wireless network. The threshold setting module (8) is interactively connected to the acquisition module (4) via a wireless network. The threshold setting module (8) is used to receive the marking processing information from the marking module (7), obtain the control authority of the marked object, and adjust the acquisition frequency of the marked object in the next cycle according to the preset adjustment standard.

10. A remote control system for a digital power supply according to claim 1, characterized in that, The energy storage module (1), communication module (2) and acquisition module (4) are interconnected via a wireless network. The communication module (2) is interconnected with the command control module (3) via a wireless network. The acquisition module (4) is interconnected with the control unit (5) via a wireless network. The command control module (3), control unit (5) and data storage module (6) are interconnected via a wireless network.

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